class_docstr: Meter to calculate top-k accuracy for single label/ multi label
  image classification task.
class_docstr_tok: ["Meter","to","calculate","top-k","accuracy","for","single","label\/","multi","label","image","classification","task","."]
code: def reset(self):
      self._total_correct_predictions_k = torch.zeros(len(self._topk))
      self._total_sample_count = torch.zeros(1)
      self._curr_correct_predictions_k = torch.zeros(len(self._topk))
      self._curr_sample_count = torch.zeros(1)
  
file_path: ClassyVision/classy_vision/meters/accuracy_meter.py
imports: from typing import Any, Dict, Sequence
  import torch
  from classy_vision.generic.distributed_util import all_reduce_sum
  from classy_vision.generic.util import is_pos_int, maybe_convert_to_one_hot
  from classy_vision.meters import ClassyMeter
  from .None import register_meter
label_desc: Use image node_id 9 for calling the AccuracyMeter obj's underlying member method code with example usage: obj.reset() without return types